Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations505354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.5 MiB
Average record size in memory136.0 B

Variable types

Numeric16
Categorical1

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Avg_Hillshade is highly overall correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Distance_to_Water is highly overall correlated with Horizontal_Distance_To_Hydrology and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Soil_Type and 1 other fieldsHigh correlation
Elevation_x_Slope is highly overall correlated with SlopeHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 3 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Hydro_Road_Fire_DistanceHigh correlation
Hydro_Road_Fire_Distance is highly overall correlated with Horizontal_Distance_To_Fire_Points and 1 other fieldsHigh correlation
Slope is highly overall correlated with Avg_Hillshade and 1 other fieldsHigh correlation
Soil_Type is highly overall correlated with ElevationHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Distance_to_Water and 1 other fieldsHigh correlation
Wilderness_Area is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Hydrology has 21630 (4.3%) zeros Zeros
Vertical_Distance_To_Hydrology has 34287 (6.8%) zeros Zeros
Distance_to_Water has 21630 (4.3%) zeros Zeros

Reproduction

Analysis started2025-06-09 19:35:12.419473
Analysis finished2025-06-09 19:35:54.435196
Duration42.02 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2951.5881
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:54.518429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2381
Q12814
median2987
Q33142
95-th percentile3322
Maximum3858
Range1999
Interquartile range (IQR)328

Descriptive statistics

Standard deviation275.48424
Coefficient of variation (CV)0.093334245
Kurtosis1.0931128
Mean2951.5881
Median Absolute Deviation (MAD)163
Skewness-0.89179409
Sum1.4915968 × 109
Variance75891.569
MonotonicityNot monotonic
2025-06-09T22:35:54.601825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2968 1625
 
0.3%
2962 1613
 
0.3%
2991 1613
 
0.3%
2972 1609
 
0.3%
2978 1603
 
0.3%
2975 1599
 
0.3%
2988 1554
 
0.3%
2955 1524
 
0.3%
2965 1519
 
0.3%
2952 1514
 
0.3%
Other values (1968) 489581
96.9%
ValueCountFrequency (%)
1859 1
 
< 0.1%
1860 1
 
< 0.1%
1861 1
 
< 0.1%
1863 1
 
< 0.1%
1866 1
 
< 0.1%
1867 1
 
< 0.1%
1868 1
 
< 0.1%
1871 3
< 0.1%
1872 4
< 0.1%
1873 1
 
< 0.1%
ValueCountFrequency (%)
3858 2
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3853 1
 
< 0.1%
3852 1
 
< 0.1%
3851 2
 
< 0.1%
3850 1
 
< 0.1%
3849 4
< 0.1%
3848 1
 
< 0.1%
3846 6
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.78752
Minimum0
Maximum360
Zeros4536
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:54.683116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q157
median124
Q3263
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)206

Descriptive statistics

Standard deviation112.6629
Coefficient of variation (CV)0.72785521
Kurtosis-1.2289254
Mean154.78752
Median Absolute Deviation (MAD)84
Skewness0.41649173
Sum78222490
Variance12692.929
MonotonicityNot monotonic
2025-06-09T22:35:54.768631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 5854
 
1.2%
0 4536
 
0.9%
90 4111
 
0.8%
135 3443
 
0.7%
63 3320
 
0.7%
315 3236
 
0.6%
18 3077
 
0.6%
72 3071
 
0.6%
27 3071
 
0.6%
34 2521
 
0.5%
Other values (351) 469114
92.8%
ValueCountFrequency (%)
0 4536
0.9%
1 1506
 
0.3%
2 1709
 
0.3%
3 1718
 
0.3%
4 2023
0.4%
5 1844
0.4%
6 1981
0.4%
7 1951
0.4%
8 1983
0.4%
9 2229
0.4%
ValueCountFrequency (%)
360 49
 
< 0.1%
359 1220
0.2%
358 1566
0.3%
357 1643
0.3%
356 1801
0.4%
355 1725
0.3%
354 1784
0.4%
353 1732
0.3%
352 1782
0.4%
351 1938
0.4%

Slope
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767395
Minimum0
Maximum66
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:54.855760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4737982
Coefficient of variation (CV)0.5428622
Kurtosis0.75038989
Mean13.767395
Median Absolute Deviation (MAD)5
Skewness0.85685891
Sum6957408
Variance55.857659
MonotonicityNot monotonic
2025-06-09T22:35:54.937529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 30511
 
6.0%
11 30123
 
6.0%
12 29324
 
5.8%
9 29017
 
5.7%
13 28187
 
5.6%
8 27610
 
5.5%
14 25932
 
5.1%
15 24691
 
4.9%
7 24304
 
4.8%
6 22768
 
4.5%
Other values (57) 232887
46.1%
ValueCountFrequency (%)
0 621
 
0.1%
1 3485
 
0.7%
2 7301
 
1.4%
3 10951
 
2.2%
4 15310
3.0%
5 19417
3.8%
6 22768
4.5%
7 24304
4.8%
8 27610
5.5%
9 29017
5.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
 
< 0.1%
61 4
< 0.1%
60 2
 
< 0.1%
59 3
< 0.1%
58 1
 
< 0.1%
57 7
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.15709
Minimum0
Maximum1397
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:55.018045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median216
Q3379
95-th percentile674
Maximum1397
Range1397
Interquartile range (IQR)271

Descriptive statistics

Standard deviation211.08873
Coefficient of variation (CV)0.79309826
Kurtosis1.6070835
Mean266.15709
Median Absolute Deviation (MAD)131
Skewness1.188776
Sum1.3450355 × 108
Variance44558.451
MonotonicityNot monotonic
2025-06-09T22:35:55.097557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 29993
 
5.9%
0 21630
 
4.3%
150 18274
 
3.6%
60 16778
 
3.3%
67 13454
 
2.7%
42 12960
 
2.6%
108 12702
 
2.5%
85 12156
 
2.4%
90 9757
 
1.9%
120 9343
 
1.8%
Other values (541) 348307
68.9%
ValueCountFrequency (%)
0 21630
4.3%
30 29993
5.9%
42 12960
2.6%
60 16778
3.3%
67 13454
2.7%
85 12156
2.4%
90 9757
 
1.9%
95 8120
 
1.6%
108 12702
2.5%
120 9343
 
1.8%
ValueCountFrequency (%)
1397 1
< 0.1%
1390 2
< 0.1%
1383 2
< 0.1%
1382 1
< 0.1%
1376 1
< 0.1%
1371 1
< 0.1%
1370 1
< 0.1%
1369 1
< 0.1%
1368 2
< 0.1%
1361 2
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct692
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.002424
Minimum-159
Maximum601
Zeros34287
Zeros (%)6.8%
Negative45771
Negative (%)9.1%
Memory size3.9 MiB
2025-06-09T22:35:55.179488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-159
5-th percentile-7
Q17
median29
Q367
95-th percentile164
Maximum601
Range760
Interquartile range (IQR)60

Descriptive statistics

Standard deviation57.90111
Coefficient of variation (CV)1.2586535
Kurtosis5.8017203
Mean46.002424
Median Absolute Deviation (MAD)26
Skewness1.8979445
Sum23247509
Variance3352.5386
MonotonicityNot monotonic
2025-06-09T22:35:55.261602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34287
 
6.8%
3 8388
 
1.7%
10 8084
 
1.6%
7 7807
 
1.5%
13 7707
 
1.5%
6 7694
 
1.5%
4 7529
 
1.5%
5 6717
 
1.3%
16 6715
 
1.3%
23 6539
 
1.3%
Other values (682) 403887
79.9%
ValueCountFrequency (%)
-159 2
< 0.1%
-158 1
< 0.1%
-156 1
< 0.1%
-154 1
< 0.1%
-153 2
< 0.1%
-152 2
< 0.1%
-151 1
< 0.1%
-150 1
< 0.1%
-149 1
< 0.1%
-147 1
< 0.1%
ValueCountFrequency (%)
601 1
 
< 0.1%
599 1
 
< 0.1%
598 2
< 0.1%
597 3
< 0.1%
595 2
< 0.1%
592 1
 
< 0.1%
591 1
 
< 0.1%
590 2
< 0.1%
589 3
< 0.1%
588 3
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

High correlation 

Distinct5785
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.98
Minimum0
Maximum7117
Zeros96
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:55.341604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391
Q11140
median2078
Q33475
95-th percentile5571
Maximum7117
Range7117
Interquartile range (IQR)2335

Descriptive statistics

Standard deviation1600.4154
Coefficient of variation (CV)0.65807093
Kurtosis-0.53780115
Mean2431.98
Median Absolute Deviation (MAD)1085
Skewness0.65607631
Sum1.2290108 × 109
Variance2561329.4
MonotonicityNot monotonic
2025-06-09T22:35:55.425385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1078
 
0.2%
618 882
 
0.2%
900 821
 
0.2%
1020 805
 
0.2%
990 777
 
0.2%
960 764
 
0.2%
390 763
 
0.2%
1140 736
 
0.1%
1050 726
 
0.1%
750 725
 
0.1%
Other values (5775) 497277
98.4%
ValueCountFrequency (%)
0 96
 
< 0.1%
30 267
0.1%
42 153
 
< 0.1%
60 280
0.1%
67 249
< 0.1%
85 293
0.1%
90 331
0.1%
95 317
0.1%
108 537
0.1%
120 559
0.1%
ValueCountFrequency (%)
7117 1
< 0.1%
7116 1
< 0.1%
7112 1
< 0.1%
7097 1
< 0.1%
7092 1
< 0.1%
7087 2
< 0.1%
7082 1
< 0.1%
7079 1
< 0.1%
7078 2
< 0.1%
7069 1
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.28271
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:55.522655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1199
median218
Q3231
95-th percentile245
Maximum254
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation26.629387
Coefficient of variation (CV)0.12544303
Kurtosis2.1182007
Mean212.28271
Median Absolute Deviation (MAD)15
Skewness-1.2439666
Sum1.0727792 × 108
Variance709.12424
MonotonicityNot monotonic
2025-06-09T22:35:55.615171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 10413
 
2.1%
228 10242
 
2.0%
224 10051
 
2.0%
230 10049
 
2.0%
223 9782
 
1.9%
222 9720
 
1.9%
233 9416
 
1.9%
227 9382
 
1.9%
225 9243
 
1.8%
221 9239
 
1.8%
Other values (197) 407817
80.7%
ValueCountFrequency (%)
0 13
< 0.1%
36 1
 
< 0.1%
46 2
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
56 5
 
< 0.1%
57 2
 
< 0.1%
ValueCountFrequency (%)
254 1641
 
0.3%
253 1822
 
0.4%
252 2123
0.4%
251 2409
0.5%
250 2766
0.5%
249 3116
0.6%
248 3249
0.6%
247 3691
0.7%
246 4069
0.8%
245 4560
0.9%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.2563
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:55.697450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.58077
Coefficient of variation (CV)0.087705341
Kurtosis2.4443385
Mean223.2563
Median Absolute Deviation (MAD)12
Skewness-1.1404263
Sum1.1282347 × 108
Variance383.40657
MonotonicityNot monotonic
2025-06-09T22:35:55.790966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 12314
 
2.4%
228 12230
 
2.4%
233 12063
 
2.4%
230 11975
 
2.4%
229 11849
 
2.3%
234 11805
 
2.3%
227 11601
 
2.3%
226 11594
 
2.3%
223 11564
 
2.3%
225 11495
 
2.3%
Other values (175) 386864
76.6%
ValueCountFrequency (%)
0 5
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
53 2
 
< 0.1%
63 1
 
< 0.1%
64 1
 
< 0.1%
68 1
 
< 0.1%
71 1
 
< 0.1%
ValueCountFrequency (%)
254 3981
0.8%
253 4664
0.9%
252 5492
1.1%
251 5864
1.2%
250 6488
1.3%
249 6356
1.3%
248 6907
1.4%
247 7617
1.5%
246 7453
1.5%
245 7352
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.57913
Minimum0
Maximum254
Zeros1266
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:55.877657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1120
median143
Q3168
95-th percentile203
Maximum254
Range254
Interquartile range (IQR)48

Descriptive statistics

Standard deviation37.78155
Coefficient of variation (CV)0.26498653
Kurtosis0.54411121
Mean142.57913
Median Absolute Deviation (MAD)24
Skewness-0.29140078
Sum72052935
Variance1427.4455
MonotonicityNot monotonic
2025-06-09T22:35:55.964950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 6582
 
1.3%
145 6474
 
1.3%
138 6384
 
1.3%
146 6243
 
1.2%
142 6189
 
1.2%
136 6166
 
1.2%
139 6153
 
1.2%
149 6078
 
1.2%
135 6035
 
1.2%
150 6026
 
1.2%
Other values (245) 443024
87.7%
ValueCountFrequency (%)
0 1266
0.3%
1 14
 
< 0.1%
2 15
 
< 0.1%
3 15
 
< 0.1%
4 18
 
< 0.1%
5 18
 
< 0.1%
6 24
 
< 0.1%
7 28
 
< 0.1%
8 20
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
254 4
 
< 0.1%
253 8
 
< 0.1%
252 16
 
< 0.1%
251 10
 
< 0.1%
250 15
 
< 0.1%
249 35
< 0.1%
248 41
< 0.1%
247 54
< 0.1%
246 68
< 0.1%
245 78
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

High correlation 

Distinct5827
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.1361
Minimum0
Maximum7173
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.046954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile408
Q11024
median1725
Q32592
95-th percentile5089
Maximum7173
Range7173
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation1357.8179
Coefficient of variation (CV)0.67548557
Kurtosis1.5080032
Mean2010.1361
Median Absolute Deviation (MAD)771
Skewness1.266097
Sum1.0158303 × 109
Variance1843669.5
MonotonicityNot monotonic
2025-06-09T22:35:56.134279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 1244
 
0.2%
541 981
 
0.2%
607 930
 
0.2%
942 856
 
0.2%
997 844
 
0.2%
700 833
 
0.2%
726 798
 
0.2%
752 782
 
0.2%
900 774
 
0.2%
960 768
 
0.2%
Other values (5817) 496544
98.3%
ValueCountFrequency (%)
0 45
 
< 0.1%
30 184
< 0.1%
42 183
< 0.1%
60 182
< 0.1%
67 370
0.1%
85 183
< 0.1%
90 182
< 0.1%
95 366
0.1%
108 369
0.1%
120 180
< 0.1%
ValueCountFrequency (%)
7173 1
< 0.1%
7172 1
< 0.1%
7168 1
< 0.1%
7150 1
< 0.1%
7145 1
< 0.1%
7142 1
< 0.1%
7141 2
< 0.1%
7140 1
< 0.1%
7131 1
< 0.1%
7126 1
< 0.1%

Cover_Type
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0589349
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.195526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3893957
Coefficient of variation (CV)0.67481283
Kurtosis4.9724135
Mean2.0589349
Median Absolute Deviation (MAD)0
Skewness2.2809236
Sum1040491
Variance1.9304204
MonotonicityNot monotonic
2025-06-09T22:35:56.243529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 254165
50.3%
1 178709
35.4%
3 28058
 
5.6%
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
ValueCountFrequency (%)
1 178709
35.4%
2 254165
50.3%
3 28058
 
5.6%
4 2747
 
0.5%
5 9292
 
1.8%
6 14851
 
2.9%
7 17532
 
3.5%
ValueCountFrequency (%)
7 17532
 
3.5%
6 14851
 
2.9%
5 9292
 
1.8%
4 2747
 
0.5%
3 28058
 
5.6%
2 254165
50.3%
1 178709
35.4%

Distance_to_Water
Real number (ℝ)

High correlation  Zeros 

Distinct48761
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.713
Minimum0
Maximum1418.9168
Zeros21630
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.312036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108.16654
median225.85836
Q3390.10383
95-th percentile689.3271
Maximum1418.9168
Range1418.9168
Interquartile range (IQR)281.93729

Descriptive statistics

Standard deviation215.62566
Coefficient of variation (CV)0.7906688
Kurtosis1.604842
Mean272.713
Median Absolute Deviation (MAD)135.58655
Skewness1.1832935
Sum1.3781661 × 108
Variance46494.427
MonotonicityNot monotonic
2025-06-09T22:35:56.393344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21630
 
4.3%
30 4151
 
0.8%
30.14962686 3374
 
0.7%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
Other values (48751) 459186
90.9%
ValueCountFrequency (%)
0 21630
4.3%
30 4151
 
0.8%
30.01666204 3338
 
0.7%
30.06659276 3333
 
0.7%
30.14962686 3374
 
0.7%
30.2654919 2748
 
0.5%
30.41381265 2308
 
0.5%
30.59411708 2177
 
0.4%
30.8058436 1810
 
0.4%
31.04834939 1299
 
0.3%
ValueCountFrequency (%)
1418.91684 1
< 0.1%
1413.295794 1
< 0.1%
1411.059531 1
< 0.1%
1407.459058 1
< 0.1%
1397.781456 1
< 0.1%
1395.054838 1
< 0.1%
1394.460469 1
< 0.1%
1390.893598 1
< 0.1%
1389.705724 1
< 0.1%
1384.234084 1
< 0.1%

Avg_Hillshade
Real number (ℝ)

High correlation 

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.70605
Minimum31.666667
Maximum213.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.489600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.666667
5-th percentile165.33333
Q1185.66667
median195.33333
Q3202.66667
95-th percentile211
Maximum213.66667
Range182
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.393874
Coefficient of variation (CV)0.074693421
Kurtosis3.0701699
Mean192.70605
Median Absolute Deviation (MAD)8.3333333
Skewness-1.3616708
Sum97384772
Variance207.18361
MonotonicityNot monotonic
2025-06-09T22:35:56.578110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199.3333333 6583
 
1.3%
198 6186
 
1.2%
198.6666667 6168
 
1.2%
201.3333333 5970
 
1.2%
196.3333333 5963
 
1.2%
200 5952
 
1.2%
195.6666667 5878
 
1.2%
195.3333333 5798
 
1.1%
196.6666667 5781
 
1.1%
200.6666667 5696
 
1.1%
Other values (374) 445379
88.1%
ValueCountFrequency (%)
31.66666667 1
< 0.1%
34.33333333 1
< 0.1%
55.33333333 1
< 0.1%
59.66666667 1
< 0.1%
61.66666667 2
< 0.1%
63.66666667 1
< 0.1%
64.33333333 1
< 0.1%
70 1
< 0.1%
73.33333333 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
213.6666667 247
 
< 0.1%
213.3333333 1927
0.4%
213 2939
0.6%
212.6666667 3181
0.6%
212.3333333 3415
0.7%
212 3115
0.6%
211.6666667 3447
0.7%
211.3333333 3739
0.7%
211 3372
0.7%
210.6666667 4121
0.8%

Hydro_Road_Fire_Distance
Real number (ℝ)

High correlation 

Distinct12679
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4708.2733
Minimum108
Maximum13141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.662335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile1448
Q12800
median4270
Q36223
95-th percentile9595
Maximum13141
Range13033
Interquartile range (IQR)3423

Descriptive statistics

Standard deviation2478.0415
Coefficient of variation (CV)0.52631642
Kurtosis-0.029145393
Mean4708.2733
Median Absolute Deviation (MAD)1624
Skewness0.7353494
Sum2.3793447 × 109
Variance6140689.8
MonotonicityNot monotonic
2025-06-09T22:35:56.746337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2950 134
 
< 0.1%
3001 125
 
< 0.1%
2956 123
 
< 0.1%
2914 122
 
< 0.1%
5340 121
 
< 0.1%
3118 118
 
< 0.1%
3020 117
 
< 0.1%
3547 116
 
< 0.1%
3194 116
 
< 0.1%
2149 116
 
< 0.1%
Other values (12669) 504146
99.8%
ValueCountFrequency (%)
108 1
< 0.1%
115 1
< 0.1%
125 1
< 0.1%
150 2
< 0.1%
152 1
< 0.1%
157 1
< 0.1%
162 1
< 0.1%
164 1
< 0.1%
166 2
< 0.1%
180 1
< 0.1%
ValueCountFrequency (%)
13141 1
< 0.1%
13134 1
< 0.1%
13127 1
< 0.1%
13124 1
< 0.1%
13121 1
< 0.1%
13114 1
< 0.1%
13113 1
< 0.1%
13111 1
< 0.1%
13110 2
< 0.1%
13109 2
< 0.1%

Elevation_x_Slope
Real number (ℝ)

High correlation 

Distinct37825
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40100.483
Minimum0
Maximum204880
Zeros621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.827844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11242.6
Q124696
median37308
Q352513
95-th percentile78532
Maximum204880
Range204880
Interquartile range (IQR)27817

Descriptive statistics

Standard deviation20984.36
Coefficient of variation (CV)0.52329443
Kurtosis0.91042054
Mean40100.483
Median Absolute Deviation (MAD)13658
Skewness0.80115176
Sum2.026494 × 1010
Variance4.4034335 × 108
MonotonicityNot monotonic
2025-06-09T22:35:56.904722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 621
 
0.1%
38376 163
 
< 0.1%
35508 159
 
< 0.1%
39312 157
 
< 0.1%
29520 154
 
< 0.1%
29780 146
 
< 0.1%
38844 146
 
< 0.1%
26748 143
 
< 0.1%
29590 143
 
< 0.1%
48960 142
 
< 0.1%
Other values (37815) 503380
99.6%
ValueCountFrequency (%)
0 621
0.1%
1929 1
 
< 0.1%
1940 1
 
< 0.1%
2012 1
 
< 0.1%
2088 2
 
< 0.1%
2100 1
 
< 0.1%
2103 1
 
< 0.1%
2121 1
 
< 0.1%
2122 1
 
< 0.1%
2129 1
 
< 0.1%
ValueCountFrequency (%)
204880 1
< 0.1%
203808 1
< 0.1%
201110 1
< 0.1%
195796 1
< 0.1%
193579 1
< 0.1%
189297 1
< 0.1%
185673 1
< 0.1%
184912 1
< 0.1%
179312 1
< 0.1%
177320 1
< 0.1%

Soil_Type
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.67163
Minimum0
Maximum39
Zeros3031
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2025-06-09T22:35:56.976234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median28
Q330
95-th percentile37
Maximum39
Range39
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1508803
Coefficient of variation (CV)0.38657584
Kurtosis-0.28933434
Mean23.67163
Median Absolute Deviation (MAD)4
Skewness-0.76966211
Sum11962553
Variance83.738611
MonotonicityNot monotonic
2025-06-09T22:35:57.054238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
28 115174
22.8%
22 48686
9.6%
31 46440
9.2%
32 33897
 
6.7%
29 30170
 
6.0%
11 29971
 
5.9%
9 26929
 
5.3%
21 25682
 
5.1%
30 24253
 
4.8%
23 16959
 
3.4%
Other values (30) 107193
21.2%
ValueCountFrequency (%)
0 3031
 
0.6%
1 4918
 
1.0%
2 2531
 
0.5%
3 7619
 
1.5%
4 1597
 
0.3%
5 6575
 
1.3%
6 105
 
< 0.1%
7 179
 
< 0.1%
8 1147
 
0.2%
9 26929
5.3%
ValueCountFrequency (%)
39 6834
 
1.4%
38 10406
 
2.1%
37 13519
 
2.7%
36 298
 
0.1%
35 119
 
< 0.1%
34 1391
 
0.3%
33 706
 
0.1%
32 33897
6.7%
31 46440
9.2%
30 24253
4.8%

Wilderness_Area
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.0
260796 
2.0
199161 
3.0
36968 
1.0
 
8429

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1516062
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 260796
51.6%
2.0 199161
39.4%
3.0 36968
 
7.3%
1.0 8429
 
1.7%

Length

2025-06-09T22:35:57.128747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-09T22:35:57.200099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 260796
51.6%
2.0 199161
39.4%
3.0 36968
 
7.3%
1.0 8429
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 766150
50.5%
. 505354
33.3%
2 199161
 
13.1%
3 36968
 
2.4%
1 8429
 
0.6%

Interactions

2025-06-09T22:35:51.625692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:24.110220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.782632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.444932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.183948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:31.060340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:32.934350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:34.811602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.606964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:38.420792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:40.263767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:42.029866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.589358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:45.180549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:48.498855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:50.084462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:51.726212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:24.240694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.882141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.559163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.304598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:31.174860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:33.061170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:34.925112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.722729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:38.536129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:40.371575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:42.127380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.689633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:45.279821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:48.598364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:50.181980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:51.829644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:24.344918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.980399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.660681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.421110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:31.296864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:33.173569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:35.035632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.834601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:38.652110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:40.479513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:42.222589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.793492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:45.381087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:48.695881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:50.276830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:51.926156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:24.445031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:26.079909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.759954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.534362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:31.412375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:33.293268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:35.142453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.943623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:38.763625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:40.579594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:42.316112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.890010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:45.484371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:48.794785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:50.370240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:52.029671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:24.572175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:26.184640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.870323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.652988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:24.777512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:26.397162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.085700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.889600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:31.766029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:50.764238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:26.696858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.390815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:32.105387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:39.453057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:41.264069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:42.909294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:44.482795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:49.387916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:26.805368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.501093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:30.362474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:32.226641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:49.496410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:52.745231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.276611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:26.907553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.610223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:30.476348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:32.342489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:34.220371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.045383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:37.872032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:39.691494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:41.524618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.098627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:47.999848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:51.146711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:52.850145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.377128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.012847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.725511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:30.592506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:48.098360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:51.245225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:52.951657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.482426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.116356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:28.845528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:30.711017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:32.578294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:34.459803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:48.194873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:49.791187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:51.341693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:53.054435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:25.583525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:28.965921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:32.696425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:38.197430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-09T22:35:25.680411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:27.336406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:29.076439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:30.942091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:32.811826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:34.693249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:36.489669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:38.306674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:40.148247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:41.927357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:43.490840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:45.079035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:48.391300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:49.981218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-09T22:35:51.530188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-09T22:35:57.255102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectAvg_HillshadeCover_TypeDistance_to_WaterElevationElevation_x_SlopeHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHydro_Road_Fire_DistanceSlopeSoil_TypeVertical_Distance_To_HydrologyWilderness_Area
Aspect1.0000.4360.0400.0030.0320.0700.632-0.4150.414-0.110-0.0030.019-0.0470.0680.0140.0720.162
Avg_Hillshade0.4361.000-0.0780.0250.193-0.4990.655-0.1940.9860.0180.0360.2190.158-0.5230.022-0.1090.138
Cover_Type0.040-0.0781.0000.002-0.4930.084-0.028-0.009-0.063-0.121-0.007-0.226-0.2230.167-0.2240.1210.482
Distance_to_Water0.0030.0250.0021.0000.2510.0790.036-0.0470.0220.0650.9990.0620.1430.0350.1920.6470.106
Elevation0.0320.193-0.4930.2511.000-0.0230.0730.0260.1860.1290.2610.4170.368-0.1750.5360.0600.543
Elevation_x_Slope0.070-0.4990.0840.079-0.0231.000-0.181-0.119-0.443-0.1320.057-0.155-0.1780.9840.1280.3310.132
Hillshade_3pm0.6320.655-0.0280.0360.073-0.1811.000-0.8190.566-0.0830.0350.1050.030-0.187-0.0030.0360.162
Hillshade_9am-0.415-0.194-0.009-0.0470.026-0.119-0.8191.000-0.0860.126-0.0390.0070.067-0.1250.009-0.1320.196
Hillshade_Noon0.4140.986-0.0630.0220.186-0.4430.566-0.0861.0000.0200.0320.2090.152-0.4680.020-0.0990.143
Horizontal_Distance_To_Fire_Points-0.1100.018-0.1210.0650.129-0.132-0.0830.1260.0201.0000.0740.3710.735-0.1690.072-0.0390.290
Horizontal_Distance_To_Hydrology-0.0030.036-0.0070.9990.2610.0570.035-0.0390.0320.0741.0000.0720.1540.0130.1960.6240.106
Horizontal_Distance_To_Roadways0.0190.219-0.2260.0620.417-0.1550.1050.0070.2090.3710.0721.0000.880-0.2280.191-0.0320.363
Hydro_Road_Fire_Distance-0.0470.158-0.2230.1430.368-0.1780.0300.0670.1520.7350.1540.8801.000-0.2470.1750.0020.424
Slope0.068-0.5230.1670.035-0.1750.984-0.187-0.125-0.468-0.1690.013-0.228-0.2471.0000.0300.3160.206
Soil_Type0.0140.022-0.2240.1920.5360.128-0.0030.0090.0200.0720.1960.1910.1750.0301.0000.1180.459
Vertical_Distance_To_Hydrology0.072-0.1090.1210.6470.0600.3310.036-0.132-0.099-0.0390.624-0.0320.0020.3160.1181.0000.129
Wilderness_Area0.1620.1380.4820.1060.5430.1320.1620.1960.1430.2900.1060.3630.4240.2060.4590.1291.000

Missing values

2025-06-09T22:35:53.261198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-09T22:35:53.613987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsCover_TypeDistance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeSoil_TypeWilderness_Area
02596.051.03.0258.00.0510.0221.0232.0148.06279.05258.000200.3337047.07788.028.00.0
12590.056.02.0212.0-6.0390.0220.0235.0151.06225.05212.085202.0006827.05180.028.00.0
22804.0139.09.0268.065.03180.0234.0238.0135.06121.02275.770202.3339569.025236.011.00.0
32785.0155.018.0242.0118.03090.0238.0238.0122.06211.02269.236199.3339543.050130.029.00.0
42595.045.02.0153.0-1.0391.0220.0234.0150.06172.05153.003201.3336716.05190.028.00.0
52579.0132.06.0300.0-15.067.0230.0237.0140.06031.02300.375202.3336398.015474.028.00.0
62606.045.07.0270.05.0633.0222.0225.0138.06256.05270.046195.0007159.018242.028.00.0
72605.049.04.0234.07.0573.0222.0230.0144.06228.05234.105198.6677035.010420.028.00.0
82617.045.09.0240.056.0666.0223.0221.0133.06244.05246.447192.3337150.023553.028.00.0
92612.059.010.0247.011.0636.0228.0219.0124.06230.05247.245190.3337113.026120.028.00.0
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsCover_TypeDistance_to_WaterAvg_HillshadeHydro_Road_Fire_DistanceElevation_x_SlopeSoil_TypeWilderness_Area
5053443190.056.012.0190.014.01597.0228.0214.0117.01584.01190.515186.3333371.038280.030.02.0
5053453183.060.016.0162.07.01595.0231.0205.0102.01608.01162.151179.3333365.050928.030.02.0
5053463175.053.017.0134.020.01593.0227.0203.0104.01632.01135.484178.0003359.053975.030.02.0
5053473169.044.015.0108.014.01591.0222.0205.0114.01657.01108.904180.3333356.047535.030.02.0
5053483164.048.014.085.09.01590.0224.0209.0116.01681.0185.475183.0003356.044296.030.02.0
5053493158.061.013.060.013.01590.0230.0211.0111.01706.0161.392184.0003356.041054.030.02.0
5053503151.068.013.030.06.01590.0233.0214.0111.01731.0130.594186.0003351.040963.030.02.0
5053513145.056.013.00.00.01591.0228.0213.0116.01756.010.000185.6673347.040885.023.02.0
5053523140.041.016.00.00.01593.0221.0204.0114.01781.010.000179.6673374.050240.023.02.0
5053533134.029.017.030.01.01595.0211.0200.0120.01806.0130.017177.0003431.053278.023.02.0